In this paper, a new Case-Deletion strategy is proposed. This method absorbs merits o[ clustering algorithm. It overcomes the deflection of traditional deletion strategies. The experiments show that the new algorithm ...In this paper, a new Case-Deletion strategy is proposed. This method absorbs merits o[ clustering algorithm. It overcomes the deflection of traditional deletion strategies. The experiments show that the new algorithm can reduce cases greatly and can preserve competence of CBR system.展开更多
针对多模态多目标优化中种群多样性难以维持和所得等价Pareto最优解数量不足问题,提出一种融合聚类和小生境搜索的多模态多目标优化算法(multimodal multi-objective optimization algorithm with clustering and niching searching,CSS...针对多模态多目标优化中种群多样性难以维持和所得等价Pareto最优解数量不足问题,提出一种融合聚类和小生境搜索的多模态多目标优化算法(multimodal multi-objective optimization algorithm with clustering and niching searching,CSSMPIO)。首先利用基于聚类的特殊拥挤距离非支配排序方法(clustering-based special crowding distance,CSCD)初始化种群;引入自适应物种形成策略生成稳定的小生境,在不同的小生境子空间并行搜索和保持等价Pareto最优解;采用特殊拥挤距离非支配排序策略实现个体选优、精英学习策略避免过早收敛。通过在14个多模态多目标函数上进行测试,并与7种新提出的多模态多目标优化算法进行对比实验以及Wilcoxon秩和检验发现,CSSMPIO的总体性能优于对比算法。最后将算法用于基于地图的测试问题,进一步证明了算法的有效性。展开更多
A novel Support Vector Machine(SVM) ensemble approach using clustering analysis is proposed. Firstly,the positive and negative training examples are clustered through subtractive clus-tering algorithm respectively. Th...A novel Support Vector Machine(SVM) ensemble approach using clustering analysis is proposed. Firstly,the positive and negative training examples are clustered through subtractive clus-tering algorithm respectively. Then some representative examples are chosen from each of them to construct SVM components. At last,the outputs of the individual classifiers are fused through ma-jority voting method to obtain the final decision. Comparisons of performance between the proposed method and other popular ensemble approaches,such as Bagging,Adaboost and k.-fold cross valida-tion,are carried out on synthetic and UCI datasets. The experimental results show that our method has higher classification accuracy since the example distribution information is considered during en-semble through clustering analysis. It further indicates that our method needs a much smaller size of training subsets than Bagging and Adaboost to obtain satisfactory classification accuracy.展开更多
This paper presents a "cluster" based search scheme in peer-to-peer network. The idea is based on the fact that data distribution in an information society has structured feature. We designed an algorithm to...This paper presents a "cluster" based search scheme in peer-to-peer network. The idea is based on the fact that data distribution in an information society has structured feature. We designed an algorithm to cluster peers that have similar interests. When receiving a query request, a peer will preferentially forward it to another peer which belongs to the same cluster and shares more similar interests. By this way search efficiency will be remarkably improved and at the same time good resilience against peer failure (the ability to withstand peer failure) is reserved.展开更多
High-resolution and detailed regional soil spatial distribution information is increasingly needed for ecological modeling and land resource management. For areas with no point data, regional soil mapping includes two...High-resolution and detailed regional soil spatial distribution information is increasingly needed for ecological modeling and land resource management. For areas with no point data, regional soil mapping includes two steps： soil sampling and soil mapping. Because sampling over a large area is costly, efficient sampling strategies are required. A multi-grade representative sampling strategy, which designs a small number of representative samples with different representative grades to depict soil spatial variations at different scales, could be a potentially efficient sampling strategy for regional soil mapping. Additionally, a suitable soil mapping approach is needed to map regional soil variations based on a small number of samples. In this study, the multi-grade representative sampling strategy was applied and a fuzzy membership-weighted soil mapping approach was developed to map soil sand percentage and soil organic carbon （SOC） at 0-20 and 20-40 cm depths in a study area of 5 900 km2 in Anhui Province of China. First, geographical sub-areas were delineated using a parent lithology data layer. Next, fuzzy c-means clustering was applied to two climate and four terrain variables in each stratum. The clustering results （environmental cluster chains） were used to locate representative samples. Evaluations based on an independent validation sample set showed that the addition of samples with lower representativeness generally led to a decrease of root mean square error （RMSE）. The declining rates of RMSE with the addition of samples slowed down for 20-40 cm depth, but fluctuated for 0-20 cm depth. The predicted SOC maps based on the representative samples exhibited higher accuracy, especially for soil depth 20-40 cm, as compared to those based on legacy soil data. Multi-grade representative sampling could be an effective sampling strategy at a regional scale. This sampling strategy, combined with the fuzzy membership-based mapping approach, could be an optional effective framework for regional soil property mapping. A more detailed and accurate soft parent material map and the addition of environmental variables representing human activities would improve mapping accuracy.展开更多
文摘In this paper, a new Case-Deletion strategy is proposed. This method absorbs merits o[ clustering algorithm. It overcomes the deflection of traditional deletion strategies. The experiments show that the new algorithm can reduce cases greatly and can preserve competence of CBR system.
文摘针对多模态多目标优化中种群多样性难以维持和所得等价Pareto最优解数量不足问题,提出一种融合聚类和小生境搜索的多模态多目标优化算法(multimodal multi-objective optimization algorithm with clustering and niching searching,CSSMPIO)。首先利用基于聚类的特殊拥挤距离非支配排序方法(clustering-based special crowding distance,CSCD)初始化种群;引入自适应物种形成策略生成稳定的小生境,在不同的小生境子空间并行搜索和保持等价Pareto最优解;采用特殊拥挤距离非支配排序策略实现个体选优、精英学习策略避免过早收敛。通过在14个多模态多目标函数上进行测试,并与7种新提出的多模态多目标优化算法进行对比实验以及Wilcoxon秩和检验发现,CSSMPIO的总体性能优于对比算法。最后将算法用于基于地图的测试问题,进一步证明了算法的有效性。
基金the National Natural Science Foundation of China (No.60472072)the Specialized Research Foundation for the Doctoral Program of Higher Educa-tion of China (No.20040699034).
文摘A novel Support Vector Machine(SVM) ensemble approach using clustering analysis is proposed. Firstly,the positive and negative training examples are clustered through subtractive clus-tering algorithm respectively. Then some representative examples are chosen from each of them to construct SVM components. At last,the outputs of the individual classifiers are fused through ma-jority voting method to obtain the final decision. Comparisons of performance between the proposed method and other popular ensemble approaches,such as Bagging,Adaboost and k.-fold cross valida-tion,are carried out on synthetic and UCI datasets. The experimental results show that our method has higher classification accuracy since the example distribution information is considered during en-semble through clustering analysis. It further indicates that our method needs a much smaller size of training subsets than Bagging and Adaboost to obtain satisfactory classification accuracy.
文摘This paper presents a "cluster" based search scheme in peer-to-peer network. The idea is based on the fact that data distribution in an information society has structured feature. We designed an algorithm to cluster peers that have similar interests. When receiving a query request, a peer will preferentially forward it to another peer which belongs to the same cluster and shares more similar interests. By this way search efficiency will be remarkably improved and at the same time good resilience against peer failure (the ability to withstand peer failure) is reserved.
基金supported by the National Natural Science Foundation of China (Nos. 41471178, 41530749, and 41431177)the State Key Laboratory of Soil and Sustainable Agriculture, China (No. Y052010002)+2 种基金the Natural Science Research Program of Jiangsu, China (No. 14KJA170001)the National Key Technology Innovation Project for Water Pollution Control and Remediation, China (No. 2013ZX07103006)the National Basic Research Program (973 Program) of China (No. 2015CB954102)
文摘High-resolution and detailed regional soil spatial distribution information is increasingly needed for ecological modeling and land resource management. For areas with no point data, regional soil mapping includes two steps： soil sampling and soil mapping. Because sampling over a large area is costly, efficient sampling strategies are required. A multi-grade representative sampling strategy, which designs a small number of representative samples with different representative grades to depict soil spatial variations at different scales, could be a potentially efficient sampling strategy for regional soil mapping. Additionally, a suitable soil mapping approach is needed to map regional soil variations based on a small number of samples. In this study, the multi-grade representative sampling strategy was applied and a fuzzy membership-weighted soil mapping approach was developed to map soil sand percentage and soil organic carbon （SOC） at 0-20 and 20-40 cm depths in a study area of 5 900 km2 in Anhui Province of China. First, geographical sub-areas were delineated using a parent lithology data layer. Next, fuzzy c-means clustering was applied to two climate and four terrain variables in each stratum. The clustering results （environmental cluster chains） were used to locate representative samples. Evaluations based on an independent validation sample set showed that the addition of samples with lower representativeness generally led to a decrease of root mean square error （RMSE）. The declining rates of RMSE with the addition of samples slowed down for 20-40 cm depth, but fluctuated for 0-20 cm depth. The predicted SOC maps based on the representative samples exhibited higher accuracy, especially for soil depth 20-40 cm, as compared to those based on legacy soil data. Multi-grade representative sampling could be an effective sampling strategy at a regional scale. This sampling strategy, combined with the fuzzy membership-based mapping approach, could be an optional effective framework for regional soil property mapping. A more detailed and accurate soft parent material map and the addition of environmental variables representing human activities would improve mapping accuracy.
